code: def check_dimension(dimension, transform=None):
      """Turn a provided dimension description into a dimension object. Checks that the provided
      dimension falls into one of the supported types, listed below in the description of `dimension`
  
      Parameters
      ----------
      dimension: Tuple, list, or Dimension
          Search space `Dimension`. May be any of the following:
          * `(lower_bound, upper_bound)` tuple (`Real` or `Integer`)
          * `(lower_bound, upper_bound, prior)` tuple (`Real`)
          * List of categories (`Categorical`)
          * `Dimension` instance (`Real`, `Integer` or `Categorical`)
      transform: {"identity", "normalize", "onehot"} (optional)
          * `Categorical` dimensions support "onehot" or "identity". See `Categorical` documentation
            for more information
          * `Real` and `Integer` dimensions support "identity" or "normalize". See `Real` or `Integer`
            documentation for more information
  
      Returns
      -------
      dimension: Dimension
          Dimension instance created from the provided `dimension` description. If `dimension` is
          already an instance of `Dimension`, it is returned unchanged
      """
      if isinstance(dimension, Dimension):
          return dimension
      if not isinstance(dimension, (list, tuple, np.ndarray)):
          raise ValueError("Dimension has to be a list or tuple")
  
      # `Dimension` subclasses define actual `transform` defaults - Only pass `transform` if not None
      kwargs = dict(transform=transform) if transform else {}
  
      if len(dimension) == 1:
          return Categorical(dimension, **kwargs)
  
      if len(dimension) == 2:
          if any(
              [
                  isinstance(d, (str, bool)) or isinstance(d, np.bool_)
                  for d in dimension
              ]
          ):
              return Categorical(dimension, **kwargs)
          elif all(
              [isinstance(dim, numbers.Integral) for dim in dimension]
          ):
              return Integer(*dimension, **kwargs)
          elif any(
              [isinstance(dim, numbers.Real) for dim in dimension]
          ):
              return Real(*dimension, **kwargs)
  
      if len(dimension) == 3:
          # TODO: Below `any` should prolly be `all`
          if any(
              [isinstance(dim, (float, int)) for dim in dimension[:2]]
          ) and dimension[2] in [
              "uniform",
              "log-uniform",
          ]:
              return Real(*dimension, **kwargs)
          else:
              return Categorical(dimension, **kwargs)
  
      if len(dimension) > 3:
          return Categorical(dimension, **kwargs)
  
      raise ValueError(
          f"Invalid `dimension` {dimension}. See documentation for supported types"
      )
  
imports: from hyperparameter_hunter.space.dimensions import Dimension, Real, Integer, Categorical
  from hyperparameter_hunter.utils.general_utils import short_repr
  from functools import reduce
  import numbers
  import numpy
  from sys import maxsize
  from sklearn.utils import check_random_state
  from sklearn.utils.fixes import sp_version
label_desc: Use image node_id 1 for calling a global function with example usage: check_dimension(dimension, transform) and returns: dimension, Categorical, Categorical, Categorical, Real, Categorical, Integer, Real